International Research Journal of Education and Technology

In recent years, Digital Twin technology has emerged as a powerful approach for enhancing the efficiency and intelligence of modern engineering systems. In the automotive sector, it enables the creation of a virtual replica of a physical vehicle, allowing continuous monitoring, simulation, and data-driven analysis. However, conventional vehicle monitoring systems are often limited to reactive diagnostics and lack real-time predictive capabilities.

To address these challenges, this paper proposes a Digital Twin framework for smart vehicle monitoring and predictive maintenance using Internet of Things (IoT) technologies. The system integrates real-time data collected from various sensors, including speed, engine performance, fuel consumption, and environmental conditions, and synchronizes it with a virtual model of the vehicle. This enables accurate representation of vehicle behavior and supports proactive decision-making.

The proposed framework incorporates data acquisition, communication, processing, and analytics modules to ensure seamless data flow and system scalability. By leveraging cloud computing and advanced data analytics, the system can detect anomalies, predict potential failures, and optimize overall vehicle performance. This reduces unexpected breakdowns, minimizes maintenance costs, and improves operational reliability.

The implementation of the Digital Twin model demonstrates significant improvements in real-time monitoring, predictive maintenance, and system efficiency. Furthermore, it supports remote access and intelligent transportation solutions, contributing to the development of smarter and more connected vehicle ecosystems. Overall, the proposed framework highlights the potential of IoT-enabled Digital Twins in transforming traditional vehicle management into a proactive and intelligent system.

Keywords: Digital Twin, IoT, Vehicle Monitoring, Predictive Maintenance, Data Analytics, Smart Transportation